Overview

• Project: Agentic_AI — collection of small examples demonstrating LangChain runnables, tools, and agent patterns.
• Environment: Uses a .env file for API keys. Required keys: OPENWEATHER_API_KEY, TAVILY_API_KEY.

File Notes

• Agents.py: Implements an interactive "city assistant" agent with two tools: get_weather and get_news. Uses TavilyClient for news and ChatMistralAI as the LLM. Includes a human-approval middleware that prompts before each tool call. Known issue: requires the langchain-mistralai package (import may raise ModuleNotFoundError if not installed).
• main.py: Minimal entrypoint printing a greeting. Useful as a quick smoke-run: python main.py.
• newssummarizer.py: Runs a Tavily search (TavilySearchResults) then passes results through a ChatMistralAI prompt to produce a summarized bullet list.
• owntool.py: Example of defining a LangChain @tool (get_greeting) and invoking it locally. Prints tool metadata (name, description, args).
• parallelrunnable.py: Demonstrates RunnableParallel to run short and long explanations in parallel using RunnableLambda, ChatPromptTemplate, and ChatMistralAI.
• runnablepassthrough.py: Shows RunnablePassthrough combined with a code-generation prompt and an explanation prompt; composes runnables and prints both code and explanation outputs.
• sequencerunnable.py: Simple prompt | model | parser sequence using ChatMistralAI and StrOutputParser.
• toolcalling.py: Interactive example where an LLM binds to a tool (get_text_length) and the script handles tool-calls and final responses.
• streamlit_app.py: Streamlit web UI for fetching weather or news by city using the same API workflows and .env keys.

How to run

1. Create and activate a virtual environment (Windows PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1

2. Install dependencies:

pip install -r requirements.txt

3. Add a .env file with the required API keys:

OPENWEATHER_API_KEY=your_openweather_key
TAVILY_API_KEY=your_tavily_key

4. Run an example, for instance the agent:

python {filename}.py

5. Run the Streamlit interface:

streamlit run streamlit_app.py